Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure. Several implementations of OCT have been developed. In frequency domain OCT (FD-OCT), the interferometric signal between light from a reference and the back-scattered light from a sample point is recorded in the frequency domain typically either by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT). After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain the scattering profile of a sample along the OCT beam. Each scattering profile is called an axial scan, or A-scan. Cross-sectional images, called B-scans, and by extension 3D volumes, are built up from many A-scans, with the OCT beam illuminating a set of transverse locations on the sample either by scanning or field illumination.
Functional OCT can provide important clinical information that is not available in the typical intensity based structural OCT images. There have been several functional contrast enhancement methods including Doppler OCT, Phase-sensitive OCT, Polarization Sensitive OCT, Spectroscopic OCT, etc. Integration of functional extensions can greatly enhance the capabilities of OCT for a range of applications in medicine.
One of the most promising functional extensions of OCT has been the field of OCT angiography which is based on flow or motion contrast between repeated structural OCT measurements. A variety of OCT Angiography techniques have been developed including but not limited to optical microangiography (OMAG), speckle variance, phase variance, correlation mapping, and decorrelation (see for example US Patent Publication No. 2008/0025570, US Patent Publication No. 2010/0027857, US Patent Publication No. 2012/0307014, Fingler et al. “Mobility and transverse flow visualization using phase variance contrast with spectral domain optical coherence tomography” Opt. Express 2007; 15:12636-53, Mariampillai et al., “Speckle variance detection of microvasculature using swept-source optical coherence tomography”, Optics Letters 33(13), 1530-1533, 2008, An et al., “In vivo volumetric imaging of vascular perfusion within human retina and choroids with optical micro-angiography,” Opt. Express 16(15), 11438-11452, 2008, Enfield et al., “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography” (cmOCT), Biomed. Opt. Express 2(5), 1184-1193, 2011, and Jia et al. “Split-spectrum amplitude decorrelation angiography with optical coherence tomography” Optics Express 20(4) 4710-4725 (2012), the contents of all of which are hereby incorporated by reference). These techniques use the OCT data to achieve the imaging of functional vascular networks within microcirculatory tissue beds in vivo, without the use of exogenous contrast agents.
The key point of OCT angiography processing methods is to extract localized signal variations from the bulk motion signal of a background tissue by comparing OCT signals, such as B-scans, captured at different closely-spaced time points (inter-frame change analysis). Processing can be carried out on the complex OCT data (complex-based), the amplitude or intensity portion of the OCT data (intensity-based), or the phase portion of the data (phase-based). The separately processed intensity and phase information can also be combined in some approaches. One of the major applications of flow contrast techniques (e.g., intensity-based, phase-based, complex-based, etc.) has been to generate en face vasculature images of the retina (angiograms). High resolution en face visualization based on inter-frame change analysis requires high density of sampling points and hence the time required to finish such scans can be up to an order of magnitude higher compared to regular cube scans used in commercial OCT systems.
One of the major limitations of OCT angiography is the occurrence of projection artifacts, or decorrelation tail artifacts, in the OCT angiography images. Light passing through a blood vessel can be reflected, refracted, or absorbed. The light reflected from blood moving in the vessels forms the basis of optical coherence tomography angiography (OCT-A). However, the light that has passed through moving blood also encounters tissue below the blood vessel. When this light strikes the deeper layers in the eye, such as the retinal pigment epithelium (RPE) layer, it is reflected back to the OCT instrument. The light that has passed through the blood vessels changes over time, and so the reflected portion of this light is detected as having a decorrelation resembling blood flow. Therefore, the RPE will seem to have blood vessels that have the pattern of the overlying retinal blood vessels. This effect is referred to as the OCT-A projection artifact. OCT-A projection artifacts also occur from superficial retinal vessels, which can be seen in deeper retinal layers, or retinal and choroidal vessels which can be even seen deep in the sclera. OCT-A projection artifacts are nearly always present and seen in any structure that is located below vasculature.
One of the steps in a standard OCT angiography algorithm involves producing 2D angiography vasculature images (angiograms) of different regions or slabs of the tissue along the depth dimension from the obtained flow contrast images, which may help a user to visualize vasculature information from different retinal layers. A slab image can be generated by summing, integrating, taking the minimum or maximum value or other techniques to determine or select a single representative value of the cube motion contrast data along a particular axis between two layers (see for example U.S. Pat. Nos. 7,301,644 and 8,332,016, the contents of both of which are hereby incorporated by reference). The slabs that are most affected by decorrelation tail artifacts may include, for example, Deeper Retinal Layer (DRL), Avascular Retinal Layer (ARL), Choriocapillaris Layer (CC), and any custom slabs, especially the ones that contain the RPE.
FIG. 1 shows exemplary slab images of a superficial retinal layer (SRL) 106 and a deeper retinal layer (DRL) 108 generated as a result of segmenting OCT angiographic data, having a representative B-scan 102. The segmented B-scan 102 shows the inner limiting membrane (ILM), as indicated by reference numeral 103, the inner plexiform layer (IPL), as indicated by reference numeral 104, and the outer plexiform layer (OPL), as indicated by reference numeral 105. The upper slab image (SRL) 106 is the result of the summation of the motion contrast data between the ILM 103 and the IPL 104. The lower slab image (DRL) 108 is the result of the summation of the motion contrast data between the IPL 104 and the OPL 105. As depicted, the decorrelation tail effect, as indicated for example by reference numerals 110a-c, is visible in the image of the DRL 108. The large vessels in the SRL image 106 appear in DRL image 108 as weaker vessel artifacts.
Some of the previous methods that are used to reduce the projection artifacts include:                1) Subtracting an angiogram generated based on deeper layers from the angiogram generated from the superficial layers directly after some preprocessing steps. In this method, a true angiographic image for the subretinal space can be obtained by a simple subtraction of a scaled image obtained from the retinal space from the image obtained from the subretinal space (see for example, Zhang, Anqi, Qinqin Zhang, and Ruikang K. Wang. “Minimizing projection artifacts for accurate presentation of choroidal neovascularization in OCT micro-angiography.” Biomedical Optics Express 6.10 (2015): 4130-4143.).        2) Removing flow projection artifacts from superficial retinal blood vessels to the outer retina by first generating a binary large inner retinal vessel map based on applying a 30×30 pixel Gaussian filter. This filter removed small inner retinal vessels and masked the outer retina flow map, thus enabling the subtraction of large vessel projections. A binary outer retinal flow map was then generated by applying a 10×10 pixel Gaussian filter to remove remaining noise and mask the outer retinal flow map again to obtain a clear map. After these artifacts are removed by the mask subtraction operation, there were no longer any flow artifacts in the normally avascular outer retina (see for example, Jia, Yali, et al. “Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration.” Ophthalmology 121.7 (2014): 1435-1444, Zhang, M., Hwang, T. S., Campbell, J. P., Bailey, S. T., Wilson., D. J Huang, D., & Jia, Y. (2016). Projection-resolved optical coherence tomographic angiography. Biomedical optics express, 7(3), 816-828).        3) Slab-based approach (e.g., traditional slab-based correction method/approach), in which an artifact-present slab (topographic projection of the OCT-A volume within two defined surfaces) to be displayed without artifacts is corrected using the information of an additional slab (reference slab) defined in an upper depth position (i.e., inner locations with respect to the retina). It is assumed that the deeper slab image is generated by mixing the upper reference slab and the artifact-free slab (the unknown image to reconstruct). Artifacts can then be removed using a particular mixing model that could be of additive or multiplicative nature. Even though this traditional slab-based correction method for artifact correction works pretty well, there are still some limitations that call for a further improved approach for artifact correction. Some of the limitations associated with the traditional slab-based correction solution include 1) both the slab to be corrected and the reference slab are governed by the definition of two surfaces, which are typically defined by an automated segmentation algorithm. Possible errors in the segmentation and/or unknowns in the relationship of both slabs may lead to the removal of important information in the corrected slab (for example, actual blood vessels that are partially present in both the correction and reference slab) or the non-removal of severe artifacts (for example, those artifacts due to vessels that are not present in the reference slab due to its definition), 2) the traditional slab-based correction approach works satisfactorily for slabs generated using a maximum projection method when the surfaces describing the target and reference slabs are defined a priori based on structural information. However, this may not be the case when using, for example, a summation projection method to generate the slabs. As the decorrelation tail artifacts propagate deeper into the volume, they may overpower the real signal when using a thick slab definition. This causes the masking of the real signal in the slab and the inability to display it even after the artifacts are corrected, 3) it does not allow the development of three-dimensional techniques for the automated determination of optimal slabs for pathology visualization, and neither allows segmentation, quantification and visualization of vascular pathologies within the OCT-A volume, 4) it assumes a sub-optimal processing workflow where an artifact-correction algorithm must be executed every time there is a change in the slab definition, no matter how minimal this change is or if the definition is reverted to a previous step. This translates to increased processing time and memory as a user displaces the surfaces defining a slab to visualize particular vessels of interest.        
Therefore, what is needed is an improved artifact reduction/correction method that can overcome the above discussed problems of the previous methods and allow the generation of artifact-free OCT-A topographic images by different projection methods, as well as allowing automated slab optimization, segmentation, quantification and visualization of pathologies within an OCT-A volume.